Deriving accurate indoor location has been an increasingly important topic in the midst of mobile applications blooming. Despite a decade of research and development efforts, enabling indoor location capability remains challenging.
WiFi based indoor location technologies have attracted a lot of attention in the past decade given the wide deployment of WLAN (wireless local area network) infrastructure. There are two major industry efforts that determine locations basing on WiFi signal strength. One camp—such as “SkyHook” or GoogleMap—utilizes trilateration basing on radio propagation model to estimate mobile client distance from the known locations of Access Points (AP) and transmit power. The second camp—such as Ekahau or Qubulus—relies on storing pre-recorded calibration WiFi measurement data (“WiFi fingerprint”) so that the locations can be determined through “WiFi fingerprint” matching.
The first (“trilateration”) camp requires knowledge of WiFi Access Point locations. Skyhook's AP location database is gathered through “wardriving”, where a person drives around searching for WiFi networks. Wardrivers use a WiFi-equipped device together with a GPS (global positioning system) device to record the location of WiFi access point basing on AP proximity and current GPS fix.
The wardriving AP database has large distance error, typically at 10-20 m error range. The inaccurate AP location database is currently the performance limiting factor for the “trilateration” camp, and causes major location accuracy impact to the end user.
The second (“WiFi fingerprint”) camp requires generating a pre-calibrated WiFi fingerprint database by sampling the radio signal strength from multiple APs at dense locations, and then derives the location through fingerprint matching at fine location granularity. The common practice at the present time is to have people manually calibrating radio map at dense grid level. State of art technologies still require substantial manual input from users, leading to high deployment cost to generate high quality WiFi fingerprint database. Therefore, it is desirable to have a system and method that provides more accurate WiFi fingerprint and AP databases, while at the same time does not require a high degree of user input.